def genDhollTract_wf(nfibers=50000,
sshell=False,
wdir=None,
nthreads=1,
name='genDhollTract_wf'):
"""
Set up workflow to generate tracts with Dhollander response
"""
# Generate tract
genTract = pe.Node(mrt.Tractography(), name='genTract')
genTract.base_dir = wdir
genTract.inputs.n_tracks = np.int(nfibers * 2)
if sshell is False: # Single-shell
genTract.inputs.out_file = 'space-Template_desc-iFOD2_tractography.tck'
genTract.inputs.algorithm = 'iFOD2'
else:
genTract.inputs.out_file = 'space-Template_desc-TensorProb_tractography.tck'
genTract.inputs.algorithm = 'Tensor_Prob'
genTract.inputs.nthreads = nthreads
genTract.interface.num_threads = nthreads
# Spherical-deconvolution informed filtering of tractography
siftTract = pe.Node(mrt.SIFT(), name='siftTract')
siftTract.base_dir = wdir
siftTract.inputs.term_number = nfibers
if sshell is False: # Multi-shell
siftTract.inputs.out_file = 'space-Template_desc-iFOD2_tractography.tck'
else: # Single-shell
siftTract.inputs.out_file = 'space-Template_desc-TensorProb_tractography.tck'
siftTract.inputs.nthreads = nthreads
siftTract.interface.num_threads = nthreads
# Convert to VTK
tractConvert = pe.Node(mrt.TCKConvert(), name='convTract')
tractConvert.base_dir = wdir
if sshell is False: # Multi-shell
tractConvert.inputs.out_file = 'space-Template_desc-iFOD2_tractography.vtk'
else: # Single-shell
tractConvert.inputs.out_file = 'space-Template_desc-TensorProb_tractography.vtk'
tractConvert.inputs.nthreads = nthreads
tractConvert.interface.num_threads = nthreads
# Build workflow
workflow = pe.Workflow(name=name)
workflow.connect([(genTract, siftTract, [('out_file', 'in_file')]),
(siftTract, tractConvert, [('out_file', 'in_file')])])
return workflow
def generate_tracts(fod_wm: Path, tractogram: Path, seg_5tt: Path):
tk = mrt.Tractography()
tk.inputs.in_file = fod_wm
tk.inputs.out_file = tractogram
tk.inputs.act_file = seg_5tt
tk.inputs.backtrack = True
tk.inputs.algorithm = "iFOD2"
tk.inputs.max_length = 250
tk.inputs.out_seeds = tractogram.parent / "seeds.csv"
# tk.inputs.power = 3
tk.inputs.crop_at_gmwmi = True
tk.inputs.seed_dynamic = fod_wm
tk.inputs.select = 350000
print(tk.cmdline)
tk.run()
return tk.inputs.out_file
def generate_tracks(fod_wm: str, seg_5tt: str):
working_dir = os.path.dirname(fod_wm)
tk = mrt.Tractography()
tk.inputs.in_file = fod_wm
tk.inputs.out_file = f"{working_dir}/tractogram.tck"
tk.inputs.act_file = seg_5tt
tk.inputs.backtrack = True
tk.inputs.algorithm = "iFOD2"
tk.inputs.max_length = 250
tk.inputs.out_seeds = f"{working_dir}/seeds.nii"
# tk.inputs.power = 3
tk.inputs.crop_at_gmwmi = True
tk.inputs.seed_dynamic = fod_wm
tk.inputs.select = 350000
print(tk.cmdline)
tk.run()
return tk.inputs.out_file
def preprocess_dwi_data(self,
data,
index,
acqp,
atlas2use,
ResponseSD_algorithm='tournier',
fod_algorithm='csd',
tract_algorithm='iFOD2',
streamlines_number='10M'):
'''
preprocessing of dwi data and connectome extraction
Parameters
----------
subjects_dir = path to the subjects' folders
data: tuple |
a tuple having the path to dwi, bvecs and bvals files. It is obtained
using the function grab_data()
index: str |
Name of text file specifying the relationship between the images in
--imain and the information in --acqp and --topup. E.g. index.txt
acqp: str |
Name of text file with information about the acquisition of the images
in --imain
atlas2use: str |
The input node parcellation image
ResponseSD_algorithm (optional): str |
Select the algorithm to be used to complete the script operation;
Options are: dhollander, fa, manual, msmt_5tt, tax, tournier
(Default is 'tournier')
fod_algorithm (optional): str |
The algorithm to use for FOD estimation. (options are: csd,msmt_csd)
(Default is 'csd')
tract_algorithm (optional): str |
specify the tractography algorithm to use. Valid choices are: FACT,
iFOD1, iFOD2, Nulldist1, Nulldist2, SD_Stream, Seedtest, Tensor_Det,
Tensor_Prob (Default is 'iFOD2')
streamlines_number (optional): str |
set the desired number of streamlines (Default is '10M')
'''
if len(data[0]) != len(data[1]):
raise ValueError(
'dwi datas do not have the same shape of bvec files')
if len(data[0]) != len(data[2]):
raise ValueError(
'dwi datas do not have the same shape of bval files')
if len(data[1]) != len(data[2]):
raise ValueError(
'bvec files do not have the same shape of bvec files')
for subj in range(len(data[0])):
print('Extracting B0 volume for subject', subj)
self.roi = fsl.ExtractROI(
in_file=data[0][subj],
roi_file=os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_nodiff.nii.gz'),
t_min=0,
t_size=1)
self.roi.run()
print('Converting into .mif for subject', subj)
self.mrconvert = mrt.MRConvert()
self.mrconvert.inputs.in_file = data[0][subj]
self.mrconvert.inputs.grad_fsl = (data[1][subj], data[2][subj])
self.mrconvert.inputs.out_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] + '_dwi.mif')
self.mrconvert.run()
print('Denoising data for subject', subj)
self.denoise = mrt.DWIDenoise()
self.denoise.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] + '_dwi.mif')
self.denoise.inputs.noise = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_noise.mif')
self.denoise.inputs.out_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised.mif')
self.denoise.run()
self.denoise_convert = mrt.MRConvert()
self.denoise_convert.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised.mif')
self.denoise_convert.inputs.out_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised.nii.gz')
self.denoise_convert.run()
print('Skull stripping for subject', subj)
self.mybet = fsl.BET()
self.mybet.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_nodiff.nii.gz')
self.mybet.inputs.out_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_denoised_brain.nii.gz')
self.mybet.inputs.frac = 0.1
self.mybet.inputs.robust = True
self.mybet.inputs.mask = True
self.mybet.run()
print('Running Eddy for subject', subj)
self.eddy = Eddy()
self.eddy.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised.nii.gz')
self.eddy.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_denoised_brain_mask.nii.gz')
self.eddy.inputs.in_acqp = acqp
self.eddy.inputs.in_bvec = data[1][subj]
self.eddy.inputs.in_bval = data[2][subj]
self.eddy.inputs.out_base = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised_eddy.nii.gz')
self.eddy.run()
print('Running Bias Correction for subject', subj)
self.bias_correct = mrt.DWIBiasCorrect()
self.bias_correct.inputs.use_ants = True
self.bias_correct.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised_eddy.nii.gz')
self.bias_correct.inputs.grad_fsl = (os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised_eddy.eddy_rotated_bvecs.bvec'), data[2][subj])
self.bias_correct.inputs.bias = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] + '_bias.mif')
self.bias_correct.inputs.out_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised_eddy_unbiased.mif')
self.bias_correct.run()
print('Calculating Response function for subject', subj)
self.resp = mrt.ResponseSD()
self.resp.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised_eddy_unbiased.mif')
self.resp.inputs.algorithm = ResponseSD_algorithm
self.resp.inputs.grad_fsl = (os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised_eddy.eddy_rotated_bvecs.bvec'), data[2][subj])
self.resp.inputs.wm_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_response.txt')
self.resp.run()
print('Estimating FOD for subject', subj)
self.fod = mrt.EstimateFOD()
self.fod.inputs.algorithm = fod_algorithm
self.fod.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_dwi_denoised_eddy_unbiased.mif')
self.fod.inputs.wm_txt = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_response.txt')
self.fod.inputs.mask_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_denoised_brain_mask.nii.gz')
self.fod.inputs.grad_fsl = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_response.txt')
self.fod.run()
print('Extracting whole brain tract for subject', subj)
self.tk = mrt.Tractography()
self.tk.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] + 'fods.mif')
self.tk.inputs.roi_mask = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_denoised_brain_mask.nii.gz')
self.tk.inputs.algorithm = tract_algorithm
self.tk.inputs.seed_image = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_denoised_brain_mask.nii.gz')
self.tk.inputs.select = streamlines_number
self.tk.inputs.out_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_whole_brain_' + streamlines_number + '.tck')
self.tk.run()
print('Extracting connectome for subject', subj)
self.mat = mrt.BuildConnectome()
self.mat.inputs.in_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_whole_brain_' + streamlines_number + '.tck')
self.mat.inputs.in_parc = atlas2use
self.mat.inputs.out_file = os.path.join(
os.path.split(data[0][subj])[0] + '/' +
os.path.split(data[0][0])[1].split(".nii.gz")[0] +
'_connectome.csv')
self.mat.run()
import os
from os.path import abspath
from datetime import datetime
from IPython.display import Image
import pydot
from nipype import Workflow, Node, MapNode, Function, config
from nipype.interfaces.fsl import TOPUP, ApplyTOPUP, BET, ExtractROI, Eddy, FLIRT, FUGUE
from nipype.interfaces.fsl.maths import MathsCommand
import nipype.interfaces.utility as util
import nipype.interfaces.mrtrix3 as mrt
#Requirements for the workflow to run smoothly: All files as in NIfTI-format and named according to the following standard:
#Images are from the tonotopy DKI sequences on the 7T Philips Achieva scanner in Lund. It should work with any DKI sequence and possibly also a standard DTI but the setting for B0-corrections, epi-distortion corrections and eddy current corrections will be wrong.
#DKI file has a base name shared with bvec and bval in FSL format. E.g. "DKI.nii.gz" "DKI.bvec" and "DKI.bval".
#There is one b0-volume with reversed (P->A) phase encoding called DKIbase+_revenc. E.g. "DKI_revenc.nii.gz".
#Philips B0-map magnitude and phase offset (in Hz) images.
#One input file for topup describing the images as specified by topup.
#Set nbrOfThreads to number of available CPU threads to run the analyses.
### Need to make better revenc for the 15 version if we choose to use it (i.e. same TE and TR)
#Set to relevant directory/parameters
datadir=os.path.abspath("/Users/ling-men/Documents/MRData/testDKI")
rawDKI_base='DKI_15'
B0map_base = 'B0map'
nbrOfThreads=6
print_graph = True
acqparam_file = os.path.join(datadir,'acqparams.txt')
index_file = os.path.join(datadir,'index.txt')
####
#config.enable_debug_mode()
DKI_nii=os.path.join(datadir, rawDKI_base+'.nii.gz')
DKI_bval=os.path.join(datadir, rawDKI_base+'.bval')
dwi_file = os.path.abspath('/om/user/ksitek/exvivo/mrtrix/dwi.mif')
dti_file = os.path.abspath('/om/user/ksitek/exvivo/mrtrix/dti/dti.mif')
mask_file = os.path.join('/om/user/ksitek/exvivo/data/',
'Reg_S64550_nii_b0-slice_mask.nii.gz')
'''
tsr = mrt.FitTensor()
tsr.inputs.in_file = os.path.join('/om/user/ksitek/exvivo/mrtrix/','dwi.mif')
tsr.inputs.in_mask = os.path.join('/om/user/ksitek/exvivo/data/','Reg_S64550_nii_b0-slice_mask.nii.gz')
tsr.inputs.grad_fsl = (bvecs, bvals)
tsr.inputs.nthreads = n_threads
tsr.inputs.out_file = dti_file
tsr.cmdline
tsr.run()
'''
tk = mrt.Tractography()
tk.inputs.in_file = dwi_file
tk.inputs.algorithm = 'Tensor_Det'
tk.inputs.seed_image = mask_file
tk.inputs.grad_fsl = (bvecs, bvals)
tk.inputs.n_tracks = 1000000
tk.inputs.out_file = os.path.abspath(
'/om/user/ksitek/exvivo/mrtrix/dti/tensor_det_1m.tck')
tk.inputs.nthreads = n_threads
tk.run()
'''
import nipype.interfaces.mrtrix
tck2trk = mrtrix.MRTrix2TrackVis()
tck2trk.inputs.out_filename = os.path.abspath('/om/user/ksitek/exvivo/mrtrix/dti/mrtrix_detDTI.trk')
tck2trk.run()
'''